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r-assertr
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Provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. Similar to 'stopifnot()' but more powerful, friendly, and easier for use in pipelines.
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2025-04-22 |
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r-asremlplus
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Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Also used to display, in tables and graphs, predictions obtained using any model fitting function and to explore differences between predictions. The content falls into the following natural groupings: (i) Data, (ii) Object manipulation functions, (iii) Model modification functions, (iv) Model testing functions, (v) Model diagnostics functions, (vi) Prediction production and presentation functions, (vii) Response transformation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). A history of the fitting of a sequence of models is kept in a data frame. Procedures are available for choosing models that conform to the hierarchy or marginality principle and for displaying predictions for significant terms in tables and graphs. The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.
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2025-04-22 |
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r-asht
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Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim <doi:10.1002/bimj.201600111>], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 <doi:10.1016/j.prevetmed.2013.09.015>] or Bayer, Fay, and Graubard, 2023 <doi:10.48550/arXiv.2205.13494>). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp).
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2025-04-22 |
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r-arulesviz
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Extends package 'arules' with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.
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2025-04-22 |
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r-ascii
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Coerce R object to 'asciidoc', 'txt2tags', 'restructuredText', 'org', 'textile' or 'pandoc' syntax. Package comes with a set of drivers for 'Sweave'.
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2025-04-22 |
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r-arrayhelpers
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Some convenient functions to work with arrays.
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2025-04-22 |
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r-artool
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The aligned rank transform for nonparametric factorial ANOVAs as described by Wobbrock, Findlater, Gergle, and Higgins (2011) <doi:10.1145/1978942.1978963>. Also supports aligned rank transform contrasts as described by Elkin, Kay, Higgins, and Wobbrock (2021) <doi:10.1145/3472749.3474784>.
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2025-04-22 |
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r-ardl
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Creates complex autoregressive distributed lag (ARDL) models and constructs the underlying unrestricted and restricted error correction model (ECM) automatically, just by providing the order. It also performs the bounds-test for cointegration as described in Pesaran et al. (2001) <doi:10.1002/jae.616> and provides the multipliers and the cointegrating equation. The validity and the accuracy of this package have been verified by successfully replicating the results of Pesaran et al. (2001) in Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.
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2025-04-22 |
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r-arkhe
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A dependency-free collection of simple functions for cleaning rectangular data. This package allows to detect, count and replace values or discard rows/columns using a predicate function. In addition, it provides tools to check conditions and return informative error messages.
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2025-04-22 |
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r-areal
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A pipeable, transparent implementation of areal weighted interpolation with support for interpolating multiple variables in a single function call. These tools provide a full-featured workflow for validation and estimation that fits into both modern data management (e.g. tidyverse) and spatial data (e.g. sf) frameworks.
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2025-04-22 |
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r-apyramid
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Provides a quick method for visualizing non-aggregated line-list or aggregated census data stratified by age and one or two categorical variables (e.g. gender and health status) with any number of values. It returns a 'ggplot' object, allowing the user to further customize the output. This package is part of the 'R4Epis' project <https://r4epis.netlify.app/>.
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2025-04-22 |
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r-apsimx
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The functions in this package inspect, read, edit and run files for 'APSIM' "Next Generation" ('JSON') and 'APSIM' "Classic" ('XML'). The files with an 'apsim' extension correspond to 'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation". For more information about 'APSIM' see (<https://www.apsim.info/>) and for 'APSIM' next generation (<https://apsimnextgeneration.netlify.app/>).
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2025-04-22 |
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r-aqp
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The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps/>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
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2025-04-22 |
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r-aplot
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For many times, we are not just aligning plots as what 'cowplot' and 'patchwork' did. Users would like to align associated information that requires axes to be exactly matched in subplots, e.g. hierarchical clustering with a heatmap. This package provides utilities to aligns associated subplots to a main plot at different sides (left, right, top and bottom) with axes exactly matched.
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2025-04-22 |
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r-apatables
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A common task faced by researchers is the creation of APA style (i.e., American Psychological Association style) tables from statistical output. In R a large number of function calls are often needed to obtain all of the desired information for a single APA style table. As well, the process of manually creating APA style tables in a word processor is prone to transcription errors. This package creates Word files (.doc files) containing APA style tables for several types of analyses. Using this package minimizes transcription errors and reduces the number commands needed by the user.
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2025-04-22 |
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r-apa
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Formatter functions in the 'apa' package take the return value of a statistical test function, e.g. a call to chisq.test() and return a string formatted according to the guidelines of the APA (American Psychological Association).
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2025-04-22 |
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r-anomalize
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The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.
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2025-04-22 |
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r-anesrake
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Provides a comprehensive system for selecting variables and weighting data to match the specifications of the American National Election Studies. The package includes methods for identifying discrepant variables, raking data, and assessing the effects of the raking algorithm. It also allows automated re-raking if target variables fall outside identified bounds and allows greater user specification than other available raking algorithms. A variety of simple weighted statistics that were previously in this package (version .55 and earlier) have been moved to the package 'weights.'
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2025-04-22 |
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r-anndata
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A 'reticulate' wrapper for the Python package 'anndata'. Provides a scalable way of keeping track of data and learned annotations. Used to read from and write to the h5ad file format.
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2025-04-22 |
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r-andromeda
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Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.
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2025-04-22 |
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r-amt
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Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
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2025-04-22 |
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r-amr
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Functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.
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2025-04-22 |
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r-ameshousing
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Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.
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2025-04-22 |
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r-alsm
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Functions and Data set presented in Applied Linear Statistical Models Fifth Edition (Chapters 1-9 and 16-25), Michael H. Kutner; Christopher J. Nachtsheim; John Neter; William Li, 2005. (ISBN-10: 0071122214, ISBN-13: 978-0071122214) that do not exist in R, are gathered in this package. The whole book will be covered in the next versions.
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2025-04-22 |
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r-alphahull
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Computation of the alpha-shape and alpha-convex hull of a given sample of points in the plane. The concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull of a finite set of points. The programming is based on the duality between the Voronoi diagram and Delaunay triangulation. The package also includes a function that returns the Delaunay mesh of a given sample of points and its dual Voronoi diagram in one single object.
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2025-04-22 |
